Superiority of PCA algorithm for Facial Expression Recognition

Facial expression recognition is a rapidly growing area of research in computer vision due to its immense applications such as human-computer interaction, psychology and intelligent robotic systems etc. Various methods have been proposed in past two decades aiming to solve different issues in this aspect. Most of the researches used distinct databases for the evaluation of their techniques, so it is very challenging task to infer which one is superior. In this paper, we have implemented three methods of facial expression recognition named: Principal Component Analysis (PCA), Artificial Neural Network (ANN) and Local Binary Pattern (LBP) and evaluated their performances using JAFFE database. Keywords: Facial Expression Recognition, Emotion Recognition, JAFFE Dataset, PCA, LBP, ANN.

[1]  Ping Liu,et al.  Facial Expression Recognition via a Boosted Deep Belief Network , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Ruaa Adeeb Abdulmunem Al-falluji.  DME Detection using LBP Features , 2016 .

[3]  H. G. Virani,et al.  Effective face detection, feature extraction & neural network based approaches for facial expression recognition , 2015, 2015 International Conference on Information Processing (ICIP).

[4]  Shaogang Gong,et al.  Facial expression recognition based on Local Binary Patterns: A comprehensive study , 2009, Image Vis. Comput..

[5]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Deepak Ghimire,et al.  Geometric Feature-Based Facial Expression Recognition in Image Sequences Using Multi-Class AdaBoost and Support Vector Machines , 2013, Sensors.

[7]  Zhengyou Zhang,et al.  A Survey of Recent Advances in Face Detection , 2010 .

[8]  Friedhelm Schwenker,et al.  A Multiple Classifier System Approach for Facial Expressions in Image Sequences Utilizing GMM Supervectors , 2010, 2010 20th International Conference on Pattern Recognition.

[9]  Scott B. Baden,et al.  Accelerating Viola-Jones Face Detection to FPGA-Level Using GPUs , 2010, 2010 18th IEEE Annual International Symposium on Field-Programmable Custom Computing Machines.

[10]  Maja Pantic,et al.  Coupled Gaussian processes for pose-invariant facial expression recognition , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Junmo Kim,et al.  Development of deep learning-based facial expression recognition system , 2015, 2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV).

[12]  Nawaf Hazim Barnouti,et al.  Face Detection and Recognition Using Viola-Jones with PCA-LDA and Square Euclidean Distance , 2016 .

[13]  P. Ekman.  The argument and evidence about universals in facial expressions of emotion. , 1989 .

[14]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[15]  Markus Flierl,et al.  Facial expression recognition based on graph-preserving sparse non-negative matrix factorization , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[16]  Michael J. Lyons,et al.  Coding facial expressions with Gabor wavelets , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[17]  Nawaf Hazim Barnouti,et al.  Face Recognition Using Eigen-Face Implemented On DSP Processor , 2016 .

[18]  Li Zhang,et al.  Intelligent Facial Action and emotion recognition for humanoid robots , 2014, 2014 International Joint Conference on Neural Networks (IJCNN).